A new study published in Engineering reveals that dihydrotanshinone I (DHT), a compound from Salvia miltiorrhiza, can induce autophagic cell death in ovarian cancer by targeting sortilin 1 (SORT1). This discovery offers a potential new therapeutic strategy for treating ovarian cancer through disrupting the autophagy–lysosome pathway.

A new study published in Engineering explores how coal power flexibility can support global decarbonization efforts. Researchers find that retrofitting coal plants for flexibility enhances renewable energy integration, reduces carbon emissions, and offers economic benefits. The study highlights the importance of policy support and technological innovation in transforming coal power to meet future energy needs sustainably.

Researchers from Southeast University have developed a novel amplifying and filtering reconfigurable intelligent surface (AF-RIS) that enhances wireless communication for 6G networks. The AF-RIS achieves significant signal amplification, frequency selectivity, and beam-steering capabilities while reducing hardware costs and power consumption. This innovation could revolutionize wireless relay systems in next-generation networks.

Discover how integrating spatiotemporal resilience into unmanned systems can enhance their adaptability and performance. A new study in Engineering explores this concept, offering insights on optimizing UAV and UV deployment to boost mission success in dynamic environments.

The electrochemical oxidation of glycerol (GOR) is gaining traction as a sustainable method to convert biodiesel byproducts into valuable chemicals and fuels, aligning with global demands for renewable energy and green production. Recent advances in catalyst design, reaction mechanisms, and system integration are driving progress, though challenges in selectivity, stability, and scalability remain pivotal for industrial adoption. Researchers are tuning both noble and non-noble metal catalysts—through methods such as facet engineering and single-atom doping—to selectively steer reactions toward high-value multi-carbon products. Furthermore, coupling GOR with cathodic processes like hydrogen evolution or CO₂ reduction offers a path to lower energy use and co-produce clean fuels. Key hurdles, including mass transfer limits and feedstock compatibility, still need addressing. Proposed solutions range from advanced electrode assemblies to integrated techno-economic assessments. Moving forward, a system-level approach that balances technical performance with economic viability will be essential to accelerate GOR technology toward real-world application.

Digital twin (DT) technology is emerging as a core solution for future marine development and intelligent ocean management. The review systematically reviews digital twin applications in the marine field, clarifies its concept, proposes a five-layer framework, and summarizes key technologies, including sensing, data management, modeling, simulation, and monitoring. It highlights DT’s ability to synchronize physical marine systems with virtual models in real time, enabling simulation, prediction, optimization, and decision-making. The authors further outline challenges and development prospects, showing how DT can support deep-sea resource exploitation, offshore wind energy, marine engineering, vessel autonomy, environmental monitoring, and system reliability assessment.

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to underwater cables—and is critical for safe renewable energy development. Traditional numerical simulations and experiments require enormous computational resources, yet often fail to capture multiscale turbulence and long-term system behavior. This review highlights how machine learning (ML) is emerging as a powerful solution for analyzing, predicting, and even controlling FSI systems. Key progress spans feature detection, reduced-order modeling, physics-informed neural networks, and reinforcement-based flow control. By leveraging data-driven models to extract hidden patterns and reconstruct flow fields, ML shows promise in improving efficiency, predictive accuracy, and automated control across ocean engineering applications, positioning itself as a transformative tool for next-generation design.